Operating System Concepts - Chapter 6: Process Synchronization potx

Bounded Waiting - A bound must exist on the number of times that other processes are allowed to enter their critical sections after a process has made a request to enter its critical sec

Chapter 6: Process Synchronization

Module 6: Process Synchronization

„ Concurrent access to shared data may result in data

inconsistency

„ Maintaining data consistency requires mechanisms to

ensure the orderly execution of cooperating processes

„ Suppose that we wanted to provide a solution to the

consumer-producer problem that fills all the buffers We can do so by having an integer count that keeps track of the number of full buffers Initially, count is set to 0 It isincremented by the producer after it produces a new buffer and is decremented by the consumer after it consumes a buffer

Producer

while (true) {

/* produce an item and put in nextProduced */

while (count == BUFFER_SIZE)

; // do nothingbuffer [in] = nextProduced;

in = (in + 1) % BUFFER_SIZE;

count++;

}

while (true) {

while (count == 0)

; // do nothingnextConsumed = buffer[out];

out = (out + 1) % BUFFER_SIZE;

count ;

/* consume the item in nextConsumed}

Race Condition

register1 = count register1 = register1 + 1 count = register1

„ count could be implemented as

register2 = count register2 = register2 - 1 count = register2

„ Consider this execution interleaving with “count = 5” initially:

S0: producer execute register1 = count {register1 = 5}

S1: producer execute register1 = register1 + 1 {register1 = 6}

S2: consumer execute register2 = count {register2 = 5}

S3: consumer execute register2 = register2 - 1 {register2 = 4}

S4: producer execute count = register1 {count = 6 }

Solution to Critical-Section Problem

1 Mutual Exclusion - If process Pi is executing in its critical section,

then no other processes can be executing in their critical sections

2 Progress - If no process is executing in its critical section and

there exist some processes that wish to enter their critical section, then the selection of the processes that will enter the critical

section next cannot be postponed indefinitely

3 Bounded Waiting - A bound must exist on the number of times

that other processes are allowed to enter their critical sections after a process has made a request to enter its critical section and before that request is granted

y Assume that each process executes at a nonzero speed

y No assumption concerning relative speed of the N processes

Peterson’s Solution

„ Two process solution

„ Assume that the LOAD and STORE instructions are atomic;

that is, cannot be interrupted

„ The two processes share two variables:

z int turn;

z Boolean flag[2]

„ The variable turn indicates whose turn it is to enter the

critical section

„ The flag array is used to indicate if a process is ready to

enter the critical section flag[i] = true implies that process Pi

is ready!

Algorithm for Process P i

REMAINDER SECTION }

Synchronization Hardware

„ Many systems provide hardware support for critical section

code

„ Uniprocessors – could disable interrupts

z Currently running code would execute without preemption

z Generally too inefficient on multiprocessor systems

 Operating systems using this not broadly scalable

„ Modern machines provide special atomic hardware

instructions

 Atomic = non-interruptable

z Either test memory word and set value

z Or swap contents of two memory words

Solution using TestAndSet

„ Shared boolean variable lock., initialized to false

„ Solution:

while (true) {

while ( TestAndSet (&lock ))

; /* do nothing// critical sectionlock = FALSE;

Solution using Swap

„ Shared Boolean variable lock initialized to FALSE; Each

process has a local Boolean variable key

„ Solution:

while (true) {

key = TRUE;

while ( key == TRUE)

Swap (&lock, &key );

// critical sectionlock = FALSE;

„ Synchronization tool that does not require busy waiting

„ Semaphore S – integer variable

„ Two standard operations modify S: wait() and signal()

z Originally called P() and V()

}

z signal (S) {

S++;

}

Semaphore as General Synchronization Tool

„ Counting semaphore – integer value can range over an

unrestricted domain

„ Binary semaphore – integer value can range only between 0

and 1; can be simpler to implement

z Also known as mutex locks

„ Can implement a counting semaphore S as a binary semaphore

„ Provides mutual exclusion

z Semaphore S; // initialized to 1

z wait (S);

Critical Sectionsignal (S);

Semaphore Implementation

„ Must guarantee that no two processes can execute wait () and

signal () on the same semaphore at the same time

„ Thus, implementation becomes the critical section problem

where the wait and signal code are placed in the crticalsection

z Could now have busy waiting in critical section implementation

 But implementation code is short

 Little busy waiting if critical section rarely occupied

„ Note that applications may spend lots of time in critical sections

and therefore this is not a good solution

Semaphore Implementation with no Busy waiting

„ With each semaphore there is an associated waiting queue

Each entry in a waiting queue has two data items:

z value (of type integer)

z pointer to next record in the list

Semaphore Implementation with no Busy waiting (Cont.)

Deadlock and Starvation

„ Deadlock – two or more processes are waiting indefinitely for an

event that can be caused by only one of the waiting processes

„ Let S and Q be two semaphores initialized to 1

.

„ Starvation – indefinite blocking A process may never be removed

from the semaphore queue in which it is suspended

Classical Problems of Synchronization

„ Bounded-Buffer Problem

„ Readers and Writers Problem

„ Dining-Philosophers Problem

Bounded-Buffer Problem

„ N buffers, each can hold one item

„ Semaphore mutex initialized to the value 1

„ Semaphore full initialized to the value 0

„ Semaphore empty initialized to the value N

Bounded Buffer Problem (Cont.)

„ The structure of the producer process

Bounded Buffer Problem (Cont.)

„ The structure of the consumer process

Readers-Writers Problem

„ A data set is shared among a number of concurrent processes

z Readers – only read the data set; they do not perform any updates

z Writers – can both read and write

„ Problem – allow multiple readers to read at the same time Only

one single writer can access the shared data at the same time

Readers-Writers Problem (Cont.)

„ The structure of a writer process

while (true) {

wait (wrt) ;// writing is performedsignal (wrt) ;

}

Readers-Writers Problem (Cont.)

„ The structure of a reader process

while (true) {

wait (mutex) ; readcount ++ ;

if (readcount == 1) wait (wrt) ; signal (mutex)

// reading is performed

wait (mutex) ; readcount - - ;

if (readcount == 0) signal (wrt) ; signal (mutex) ;

}

Dining-Philosophers Problem

„ Shared data

Dining-Philosophers Problem (Cont.)

„ The structure of Philosopher i:

Problems with Semaphores

„ Incorrect use of semaphore operations:

z signal (mutex) … wait (mutex)

z wait (mutex) … wait (mutex)

z Omitting of wait (mutex) or signal (mutex) (or both)

„ A high-level abstraction that provides a convenient and effective

mechanism for process synchronization

„ Only one process may be active within the monitor at a time

monitor monitor-name {

// shared variable declarations procedure P1 (…) { … }

… procedure Pn (…) {……}

Initialization code ( ….) { … }

… }

}

Schematic view of a Monitor

Condition Variables

„ condition x, y;

„ Two operations on a condition variable:

z x.wait () – a process that invokes the operation is

suspended

z x.signal () – resumes one of processes (if any) that

invoked x.wait ()

Monitor with Condition Variables

Solution to Dining Philosophers

monitor DP

{ enum { THINKING; HUNGRY, EATING) state [5] ; condition self [5];

void pickup (int i) { state[i] = HUNGRY;

Solution to Dining Philosophers (cont)

void test (int i) {

if ( (state[(i + 4) % 5] != EATING) &&

(state[i] == HUNGRY) &&

(state[(i + 1) % 5] != EATING) ) { state[i] = EATING ;

self[i].signal () ; }

}

initialization_code() { for (int i = 0; i < 5; i++) state[i] = THINKING;

}

Solution to Dining Philosophers (cont)

„ Each philosopher I invokes the operations pickup()

and putdown() in the following sequence:

dp.pickup (i)EAT

dp.putdown (i)

Monitor Implementation Using Semaphores

„ Variables

semaphore mutex; // (initially = 1) semaphore next; // (initially = 0) int next-count = 0;

„ Each procedure F will be replaced by

signal(mutex);

Monitor Implementation

„ For each condition variable x, we have:

semaphore x-sem; // (initially = 0) int x-count = 0;

„ The operation x.wait can be implemented as:

x-count++;

if (next-count > 0) signal(next);

else signal(mutex);

wait(x-sem);

x-count ;

Monitor Implementation

„ The operation x.signal can be implemented as:

if (x-count > 0) {next-count++;

signal(x-sem);

wait(next);

next-count ;

}

Solaris Synchronization

„ Implements a variety of locks to support multitasking,

multithreading (including real-time threads), and multiprocessing

„ Uses adaptive mutexes for efficiency when protecting data from

short code segments

„ Uses condition variables and readers-writers locks when longer

sections of code need access to data

„ Uses turnstiles to order the list of threads waiting to acquire either

an adaptive mutex or reader-writer lock

Windows XP Synchronization

„ Uses interrupt masks to protect access to global resources on

uniprocessor systems

„ Uses spinlocks on multiprocessor systems

„ Also provides dispatcher objects which may act as either mutexes

and semaphores

„ Dispatcher objects may also provide events

z An event acts much like a condition variable

System Model

„ Assures that operations happen as a single logical unit of work, in

its entirety, or not at all

„ Related to field of database systems

„ Challenge is assuring atomicity despite computer system failures

„ Transaction - collection of instructions or operations that performs

single logical function

z Here we are concerned with changes to stable storage – disk

z Transaction is series of read and write operations

z Terminated by commit (transaction successful) or abort

(transaction failed) operation

z Aborted transaction must be rolled back to undo any changes it performed

Types of Storage Media

„ Volatile storage – information stored here does not survive system

crashes

z Example: main memory, cache

„ Nonvolatile storage – Information usually survives crashes

z Example: disk and tape

„ Stable storage – Information never lost

z Not actually possible, so approximated via replication or RAID to devices with independent failure modes

Goal is to assure transaction atomicity where failures cause loss of

information on volatile storage

Log-Based Recovery

„ Record to stable storage information about all modifications by a

transaction

„ Most common is write-ahead logging

z Log on stable storage, each log record describes single transaction write operation, including

 Transaction name

 Data item name

 Old value

 New value

z <Ti starts> written to log when transaction Ti starts

z <Ti commits> written when Ti commits

data occurs

Log-Based Recovery Algorithm

„ Using the log, system can handle any volatile memory errors

z Undo(Ti) restores value of all data updated by Ti

z Redo(Ti) sets values of all data in transaction Ti to new values

„ Undo(Ti) and redo(Ti) must be idempotent

z Multiple executions must have the same result as one execution

„ If system fails, restore state of all updated data via log

z If log contains <Ti starts> without <Ti commits>, undo(Ti)

z If log contains <Ti starts> and <Ti commits>, redo(Ti)

„ Log could become long, and recovery could take long

„ Checkpoints shorten log and recovery time

„ Checkpoint scheme:

1. Output all log records currently in volatile storage to stable storage

2. Output all modified data from volatile to stable storage

3. Output a log record <checkpoint> to the log on stable storage

„ Now recovery only includes Ti, such that Ti started executing

before the most recent checkpoint, and all transactions after Ti All other transactions already on stable storage

Concurrent Transactions

„ Must be equivalent to serial execution – serializability

„ Could perform all transactions in critical section

z Inefficient, too restrictive

„ Concurrency-control algorithms provide serializability

„ Consider two data items A and B

„ Consider Transactions T0 and T1

„ Execute T0, T1 atomically

„ Execution sequence called schedule

„ Atomically executed transaction order called serial schedule

„ For N transactions, there are N! valid serial schedules

Schedule 1: T0 then T1

Nonserial Schedule

„ Nonserial schedule allows overlapped execute

z Resulting execution not necessarily incorrect

„ Consider schedule S, operations Oi, Oj

z Conflict if access same data item, with at least one write

„ If Oi, Oj consecutive and operations of different transactions & Oi

and Oj don’t conflict

z Then S’ with swapped order Oj Oiequivalent to S

„ If S can become S’ via swapping nonconflicting operations

z S is conflict serializable

Schedule 2: Concurrent Serializable Schedule

Locking Protocol

„ Ensure serializability by associating lock with each data item

z Follow locking protocol for access control

„ Require every transaction on item Q acquire appropriate lock

„ If lock already held, new request may have to wait

z Similar to readers-writers algorithm

Two-phase Locking Protocol

„ Generally ensures conflict serializability

„ Each transaction issues lock and unlock requests in two phases

z Growing – obtaining locks

z Shrinking – releasing locks

„ Does not prevent deadlock

Timestamp-based Protocols

„ Select order among transactions in advance – timestamp-ordering

„ Transaction Ti associated with timestamp TS(Ti) before Ti starts

z TS(Ti) < TS(Tj) if Ti entered system before Tj

z TS can be generated from system clock or as logical counter incremented at each entry of transaction

„ Timestamps determine serializability order

z If TS(Ti) < TS(Tj), system must ensure produced schedule equivalent to serial schedule where Ti appears before Tj

Timestamp-based Protocol Implementation

„ Data item Q gets two timestamps

z W-timestamp(Q) – largest timestamp of any transaction that executed write(Q) successfully

z R-timestamp(Q) – largest timestamp of successful read(Q)

z Updated whenever read(Q) or write(Q) executed

„ Timestamp-ordering protocol assures any conflicting read and write

executed in timestamp order

„ Suppose Ti executes read(Q)

z If TS(Ti) < W-timestamp(Q), Ti needs to read value of Q that was already overwritten

 read operation rejected and Ti rolled back

z If TS(Ti) ≥ W-timestamp(Q)

read executed, R-timestamp(Q) set to

max(R-Timestamp-ordering Protocol

„ Suppose Ti executes write(Q)

z If TS(Ti) < R-timestamp(Q), value Q produced by Ti was needed previously and Ti assumed it would never be produced

 Write operation rejected, Ti rolled back

z If TS(Ti) < W-tiimestamp(Q), Ti attempting to write obsolete value of Q

 Write operation rejected and Ti rolled back

z Otherwise, write executed

„ Any rolled back transaction Ti is assigned new timestamp and

restarted

„ Algorithm ensures conflict serializability and freedom from deadlock